Results for “random-survival-forest”

Computes phylogenetic distance matrices and trees from genomic VCF or FASTA data using the fastreeR hybrid Java/Python toolkit.

Generate a complete Rust Model Context Protocol server project with tools, prompts, resources, and tests using the official rmcp SDK.

Randomly selects winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests with fair, transparent selection.

Provides a structured 5-phase workflow for bug bounty and SRC vulnerability hunting, including 19 attack-type playbooks, 305 structured payloads, 263 WAF bypass variants, and 2,887 real HackerOne case studies.

Enumerate and audit Active Directory forest trust relationships using impacket for SID filtering analysis, trust key extraction, cross-forest SID history abuse detection, and inter-realm Kerberos ticket assessment.

Proactively hunt for adversary abuse of legitimate system binaries (LOLBins) to execute malicious payloads while evading detection.

Use when inferring maximum-likelihood phylogenetic trees, selecting substitution models, running bootstrap support analyses, or performing partitioned phylogenetic analyses on sequence alignments.

Runs a relentless interview that asks every frontier question at once, round by round. Use when the user wants to be grilled on a design or map a design tree.

Executes structured recovery from a ransomware incident following NIST and CISA frameworks, including environment isolation, forensic evidence preservation, clean infrastructure rebuild, prioritized system restoration from verified backups, credential reset, and validation against re-infection.

Hunt for adversary persistence via Windows Scheduled Tasks by analyzing task creation events, suspicious task actions, and unusual scheduling patterns.

Provides on-chain verifiable randomness for coin flips, dice rolls, raffles, shuffles, and game outcomes via the DARKSOL Random Oracle API on Base.

Build an Opportunity Solution Tree (OST) to structure product discovery — map a desired outcome to opportunities, solutions, and experiments.

Structured threat modeling skill using the PASTA framework (Process for Attack Simulation and Threat Analysis) combined with Adam Shostack's 4-question framework. Use this skill whenever the user asks to do threat modeling, security analysis, map the attack surface, identify threats, or review an application for security risks — even if they don't mention PASTA or a specific framework by name. Core activities: Component Mapping (architecture + data flows), Critical Assessment (business impact prioritization), and Logic Flaw Identification (attacker mindset on business logic). Produces: component map diagram (Mermaid), data flow diagram (Mermaid), attack tree (Mermaid), STRIDE threat table, prioritized risk register, and an actionable mitigation plan. Invoke proactively for any security review, architecture review, or "what could go wrong?" session.

Build an Opportunity Solution Tree from outcomes to opportunities, solutions, and tests. Use when a stakeholder request needs problem framing before you decide what to build.

Runs an Opportunity Solution Tree workflow with a lightweight graph database and CLI, routing through outcome, opportunity, solution, and assumption phases.

Automates ingestion, normalization, deduplication, and distribution of threat intelligence feeds from STIX/TAXII, open-source, and commercial sources into SIEM platforms for real-time IOC matching and alerting.

Use when predicting RNA secondary structures, calculating minimum free energy (MFE) folds, or computing partition functions and base pairing probabilities for RNA sequences.

Executes a structured ransomware incident response from initial detection through containment, forensic analysis, decryption assessment, recovery, and post-incident hardening.

Implements immutable backup strategy using restic with S3-compatible storage and object lock for ransomware-resistant data protection, automating backup creation, integrity verification, snapshot retention, and restore testing.

Hunt for adversary abuse of Living Off the Land Binaries (LOLBins) by analyzing endpoint process creation logs for suspicious execution patterns of legitimate Windows system binaries used for malicious purposes.

Token-optimized structural code search using tree-sitter AST parsing. Use instead of reading full files when you need to understand code structure, find functions, or explore a codebase efficiently.

Explore codebases efficiently using AST-based structural search, outline, and symbol unfolding to reduce token usage.

JACKAL — First Jump pyramider. FOX v1.6's exact five-layer entry gauntlet combined with RHINO's pyramiding mechanic. Enter at 30% on a qualifying First Jump, add 40% at +10% ROE and final 30% at +20% ROE — but only after re-validating 4h trend, SM alignment, and volume. Failed scouts cost $15 instead of FOX's $50. Full pyramids capture the same upside. DSL High Water Mode (mandatory).

Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.

Generate heightmaps, map reference images, and water feature splines (rivers, lakes, oceans) from real-world geographic data (terrain_data tool). Use when the user asks to build terrain from a real location/coordinates, download a real-world heightmap, or add rivers/lakes/oceans from map data. Downloads Mapbox tiles server-side and produces UE5-compatible heightmaps + landscape splines.

Runs an autonomous optimization loop that iteratively improves an artifact against an objective and evaluator using Hypothesis Tree Refinement, with subagent executors in isolated git worktrees.

Run autonomous optimization loops that iteratively improve artifacts against evaluators using hypothesis tree refinement, without overfitting.

Biome - Fast all-in-one toolchain for web projects (linter + formatter in Rust, 100x faster than ESLint)

Expert guidance for architecting, developing, and operating SurrealDB 3, covering SurrealQL, multi-model data modeling, vector search, security, deployment, performance tuning, SDK integration, and ecosystem tools.

A cascader component for displaying and selecting tree-structured data with support for single/multiple selection, async loading, filtering, and various check strategies

Ingests, normalizes, and enriches structured and unstructured threat intelligence feeds into STIX 2.1 format, evaluating feed quality and deduplicating indicators for distribution to SIEM, firewall, and EDR platforms.

Route active project/repo memory requests into one honest packet: memory-layer choice, load-context, search-context, store-conclusions, setup-integration, or repo-packer route-out. Use when agents need searchable decisions, manifests, stable links, handoff notes, and small “read this first” packets across sessions. Route long-lived markdown knowledge bases to `llm-wiki`, structural graph memory to `graphify`, human-authored vault organization to note/vault skills, and one-shot repo packing to tools like Repomix, Gitingest, or Code2Prompt.

Grill the user relentlessly about a plan, decision, or idea. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.

Designs and governs recursive self-improvement loops where an agent mines its own failures and proposes edits to its own harness, prompts, or workflow, covering acceptance gates, diversity preservation, and the optimization ladder.

Rank an existing set of context chunks by relevance, diversity, freshness, and utility. Use when retrieval has already produced candidates that must be scored or reranked; use context-retrieval when the source corpus still needs to be searched.